Assignment 5

library(nycflights13)
library(tidyverse)

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View(flights)
View(airlines)
View(weather)
View(planes)
View(airports)

1. join + filter - Which airplanes fly LGA to XNA (1 POINT)

q1 <- flights %>%
  filter(origin == "LGA", dest == "XNA") %>%
  inner_join(planes, by = "tailnum") %>%
  select(tailnum, manufacturer, type, model, origin, dest)
q1

## # A tibble: 66 × 6
##    tailnum manufacturer         type                     model  origin dest 
##    <chr>   <chr>                <chr>                    <chr>  <chr>  <chr>
##  1 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
##  2 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
##  3 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
##  4 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
##  5 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
##  6 N737MQ  CESSNA               Fixed wing single engine 172N   LGA    XNA  
##  7 N737MQ  CESSNA               Fixed wing single engine 172N   LGA    XNA  
##  8 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
##  9 N711MQ  GULFSTREAM AEROSPACE Fixed wing multi engine  G1159B LGA    XNA  
## 10 N840MQ  CANADAIR LTD         Fixed wing multi engine  CF-5D  LGA    XNA  
## # ℹ 56 more rows

2. join - Add the airline name to the flights table (1 POINT)

q2 <- flights %>%
  inner_join(airlines, by = "carrier")
q2

## # A tibble: 336,776 × 20
##     year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
##  1  2013     1     1      517            515         2      830            819
##  2  2013     1     1      533            529         4      850            830
##  3  2013     1     1      542            540         2      923            850
##  4  2013     1     1      544            545        -1     1004           1022
##  5  2013     1     1      554            600        -6      812            837
##  6  2013     1     1      554            558        -4      740            728
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # ℹ 336,766 more rows
## # ℹ 12 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>, name <chr>

3. join + select + distinct() - Which airports have no commercial flights (1 POINT)

airports_with_flights <- flights %>%
  select(origin) %>%
  distinct() 
all_airports <- airports %>%
  select(faa)
q3 <- all_airports %>%
  anti_join(airports_with_flights, by = c("faa" = "origin"))

q3

## # A tibble: 1,455 × 1
##    faa  
##    <chr>
##  1 04G  
##  2 06A  
##  3 06C  
##  4 06N  
##  5 09J  
##  6 0A9  
##  7 0G6  
##  8 0G7  
##  9 0P2  
## 10 0S9  
## # ℹ 1,445 more rows

4. EXTRA CREDIT - (2 POINT2) - NO HELP - NO PARTIAL CREDIT

Create a table with the names of the airports with the most winds (wind_speed > 30). The table must contain only the airport name (airports$name) and no duplicate rows

Filter for wind speeds greater than 30

q4 <- weather %>%
  filter(wind_speed > 30) %>%
  select(origin) %>%
  distinct() %>%
  inner_join(airports, by = c("origin" = "faa")) %>%
  select(name) %>%
  distinct()

q4

## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia